UNDERSTANDING THE ANATOMY OF DATA-DRIVEN BUSINESS MODELS – TOWARDS AN EMPIRICAL TAXONOMY

As a consequence of the increasing digitization, massive amounts of data are created every day. While scholars and practitioners suggest that organizations can use this data to develop new data-driven business models, many organizations struggle to systematically develop such models. A fundamental challenge in this regard is presented by the limited research on data-driven business models. Accordingly, the goal of this research is to better understand data-driven business models by identifying key dimensions that can be used to distinguish them and to develop a taxonomy. As our taxonomy aims to guide future studies in a way that ultimately serves organizations, it is based on dimensions regarded to be most relevant from the practitioners’ perspective. To develop this taxonomy, we utilize an established empirical approach based on a combination of multidimensional scaling (MDS), property fitting (ProFit), and qualitative data. Our results reveal that the most important dimensions distinguish data-driven business models based on the data source utilized, the target audience, and the technological effort required. Based on these dimensions, our taxonomy distinguishes eight ideal-typical categories of data-driven business models. By providing an increased understanding regarding the topic, our results form the foundation for subsequent investigations in this new field of research

Impact of COVID-19 on cardiovascular testing in the United States versus the rest of the world

Objectives: This study sought to quantify and compare the decline in volumes of cardiovascular procedures between the United States and non-US institutions during the early phase of the coronavirus disease-2019 (COVID-19) pandemic. Background: The COVID-19 pandemic has disrupted the care of many non-COVID-19 illnesses. Reductions in diagnostic cardiovascular testing around the world have led to concerns over the implications of reduced testing for cardiovascular disease (CVD) morbidity and mortality. Methods: Data were submitted to the INCAPS-COVID (International Atomic Energy Agency Non-Invasive Cardiology Protocols Study of COVID-19), a multinational registry comprising 909 institutions in 108 countries (including 155 facilities in 40 U.S. states), assessing the impact of the COVID-19 pandemic on volumes of diagnostic cardiovascular procedures. Data were obtained for April 2020 and compared with volumes of baseline procedures from March 2019. We compared laboratory characteristics, practices, and procedure volumes between U.S. and non-U.S. facilities and between U.S. geographic regions and identified factors associated with volume reduction in the United States. Results: Reductions in the volumes of procedures in the United States were similar to those in non-U.S. facilities (68% vs. 63%, respectively; p = 0.237), although U.S. facilities reported greater reductions in invasive coronary angiography (69% vs. 53%, respectively; p < 0.001). Significantly more U.S. facilities reported increased use of telehealth and patient screening measures than non-U.S. facilities, such as temperature checks, symptom screenings, and COVID-19 testing. Reductions in volumes of procedures differed between U.S. regions, with larger declines observed in the Northeast (76%) and Midwest (74%) than in the South (62%) and West (44%). Prevalence of COVID-19, staff redeployments, outpatient centers, and urban centers were associated with greater reductions in volume in U.S. facilities in a multivariable analysis. Conclusions: We observed marked reductions in U.S. cardiovascular testing in the early phase of the pandemic and significant variability between U.S. regions. The association between reductions of volumes and COVID-19 prevalence in the United States highlighted the need for proactive efforts to maintain access to cardiovascular testing in areas most affected by outbreaks of COVID-19 infection

The genetic architecture of the human cerebral cortex

The cerebral cortex underlies our complex cognitive capabilities, yet little is known about the specific genetic loci that influence human cortical structure. To identify genetic variants that affect cortical structure, we conducted a genome-wide association meta-analysis of brain magnetic resonance imaging data from 51,665 individuals. We analyzed the surface area and average thickness of the whole cortex and 34 regions with known functional specializations. We identified 199 significant loci and found significant enrichment for loci influencing total surface area within regulatory elements that are active during prenatal cortical development, supporting the radial unit hypothesis. Loci that affect regional surface area cluster near genes in Wnt signaling pathways, which influence progenitor expansion and areal identity. Variation in cortical structure is genetically correlated with cognitive function, Parkinson's disease, insomnia, depression, neuroticism, and attention deficit hyperactivity disorder

The evolving SARS-CoV-2 epidemic in Africa: Insights from rapidly expanding genomic surveillance

INTRODUCTION Investment in Africa over the past year with regard to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) sequencing has led to a massive increase in the number of sequences, which, to date, exceeds 100,000 sequences generated to track the pandemic on the continent. These sequences have profoundly affected how public health officials in Africa have navigated the COVID-19 pandemic. RATIONALE We demonstrate how the first 100,000 SARS-CoV-2 sequences from Africa have helped monitor the epidemic on the continent, how genomic surveillance expanded over the course of the pandemic, and how we adapted our sequencing methods to deal with an evolving virus. Finally, we also examine how viral lineages have spread across the continent in a phylogeographic framework to gain insights into the underlying temporal and spatial transmission dynamics for several variants of concern (VOCs). RESULTS Our results indicate that the number of countries in Africa that can sequence the virus within their own borders is growing and that this is coupled with a shorter turnaround time from the time of sampling to sequence submission. Ongoing evolution necessitated the continual updating of primer sets, and, as a result, eight primer sets were designed in tandem with viral evolution and used to ensure effective sequencing of the virus. The pandemic unfolded through multiple waves of infection that were each driven by distinct genetic lineages, with B.1-like ancestral strains associated with the first pandemic wave of infections in 2020. Successive waves on the continent were fueled by different VOCs, with Alpha and Beta cocirculating in distinct spatial patterns during the second wave and Delta and Omicron affecting the whole continent during the third and fourth waves, respectively. Phylogeographic reconstruction points toward distinct differences in viral importation and exportation patterns associated with the Alpha, Beta, Delta, and Omicron variants and subvariants, when considering both Africa versus the rest of the world and viral dissemination within the continent. Our epidemiological and phylogenetic inferences therefore underscore the heterogeneous nature of the pandemic on the continent and highlight key insights and challenges, for instance, recognizing the limitations of low testing proportions. We also highlight the early warning capacity that genomic surveillance in Africa has had for the rest of the world with the detection of new lineages and variants, the most recent being the characterization of various Omicron subvariants. CONCLUSION Sustained investment for diagnostics and genomic surveillance in Africa is needed as the virus continues to evolve. This is important not only to help combat SARS-CoV-2 on the continent but also because it can be used as a platform to help address the many emerging and reemerging infectious disease threats in Africa. In particular, capacity building for local sequencing within countries or within the continent should be prioritized because this is generally associated with shorter turnaround times, providing the most benefit to local public health authorities tasked with pandemic response and mitigation and allowing for the fastest reaction to localized outbreaks. These investments are crucial for pandemic preparedness and response and will serve the health of the continent well into the 21st century

Discovery and Diffusion of Digital Innovations – An Analysis of Enterprise Social Networks and Data-Driven Business Models

Digital technologies radically transform today’s organizations as they permeate both innovation processes and outcomes. While the potential of digital innovations is tremendous, many companies hardly realize the extensive benefits of digital technologies so far. Furthermore, the theoretical understanding of digital innovations is limited since scholars started to challenge the assumptions made in traditional innovation research due to digital technologies’ affordances. Therefore, this thesis seeks to improve the knowledge about digital innovations by analyzing their discovery and diffusion. The discovery of innovations relates to the development of ideas, which can result in new products, processes, or business models. It is essential to investigate companies’ innovation discovery as they often struggle to create innovative ideas and existing theory rarely incorporates the increasing diversity of employees involved in these processes. Papers A and B of this thesis address these issues by examining how Enterprise Social Networks (ESNs) facilitate employees’ innovation discovery. According to Communication Visibility Theory (CVT), the consideration of ESNs is crucial in this regard as they make employees’ everyday communication permanently visible, which provides a basis for acquiring new knowledge. Paper A validates and extends the newly developed CVT. By incorporating individuals employed in diverse contexts, it empirically supports the theory’s external validity. Therefore, different companies can draw on ESNs to foster their innovation discovery, which is made possible through improvements in employees’ meta-knowledge. Besides, the paper reveals that meta-knowledge is not merely formed in the long-run, as indicated by previous research, but in the short-run as well. Interestingly, it also shows that managers can gain more meta-knowledge using ESNs compared to non-managers, which is in contrast with prior literature’s findings. Paper B investigates when employees disclose information in ESNs, which is essential to attain high communication visibility and, in this way, to facilitate the discovery of innovations. To that end, the paper transfers theory on Online Social Networks (OSNs) to the ESN context. It finds that employees’ trusting and risk beliefs are associated with their information disclosure. Additionally, the paper reveals that a company’s group and development culture influence these beliefs, with error aversion culture transmitting the effect of development culture. Innovation diffusion relates to the distribution of a novel product, process, or business model across a group of target users. It is important to better understand the diffusion of digital innovations as companies often lack knowledge about why new offerings are rejected, which limits their chances of counteracting the underlying issues. Furthermore, digital technologies impact the innovation diffusion by blurring industry boundaries and facilitating competition. Papers C and D of this thesis investigate the diffusion of digital innovations in the context of data-driven business models. This context is especially affected by new competition arising across previous boundaries and, thus, necessary to analyze as diverse organizations have high incentives to utilize their data in new ways. Paper C analyzes which dimensions substantially differentiate between distinct data-driven business models. For this purpose, it leverages practitioners’ perceptions of business models obtained from a start-up database. Based on three identified dimensions, the paper creates a taxonomy that classifies the business models into eight ideal-typical categories. The number of business models present in each category provides insights into their diffusion. By offering basic knowledge about the nature of data-driven business models, the paper can be used as a foundation for future research that seeks to dig deeper into this new field and for companies that aim at developing data-driven business models. Paper D investigates how individuals evaluate data-driven services that are offered by highly diverse companies. Based on a qualitative study, the paper shows that individuals’ perception of fit between a service and its provider is crucial for their evaluations. It also reveals the dimensions that influence this perception. Additionally, it explores the consequences that come with a perception of fit. Using these results, the paper offers a new perspective on individuals’ service evaluations, which is vital to the diffusion of the services as well as the associated business models and helps organizations in developing and promoting data-driven services

Discovery and Diffusion of Digital Innovations – An Analysis of Enterprise Social Networks and Data-Driven Business Models

Digital technologies radically transform today’s organizations as they permeate both innovation processes and outcomes. While the potential of digital innovations is tremendous, many companies hardly realize the extensive benefits of digital technologies so far. Furthermore, the theoretical understanding of digital innovations is limited since scholars started to challenge the assumptions made in traditional innovation research due to digital technologies’ affordances. Therefore, this thesis seeks to improve the knowledge about digital innovations by analyzing their discovery and diffusion. The discovery of innovations relates to the development of ideas, which can result in new products, processes, or business models. It is essential to investigate companies’ innovation discovery as they often struggle to create innovative ideas and existing theory rarely incorporates the increasing diversity of employees involved in these processes. Papers A and B of this thesis address these issues by examining how Enterprise Social Networks (ESNs) facilitate employees’ innovation discovery. According to Communication Visibility Theory (CVT), the consideration of ESNs is crucial in this regard as they make employees’ everyday communication permanently visible, which provides a basis for acquiring new knowledge. Paper A validates and extends the newly developed CVT. By incorporating individuals employed in diverse contexts, it empirically supports the theory’s external validity. Therefore, different companies can draw on ESNs to foster their innovation discovery, which is made possible through improvements in employees’ meta-knowledge. Besides, the paper reveals that meta-knowledge is not merely formed in the long-run, as indicated by previous research, but in the short-run as well. Interestingly, it also shows that managers can gain more meta-knowledge using ESNs compared to non-managers, which is in contrast with prior literature’s findings. Paper B investigates when employees disclose information in ESNs, which is essential to attain high communication visibility and, in this way, to facilitate the discovery of innovations. To that end, the paper transfers theory on Online Social Networks (OSNs) to the ESN context. It finds that employees’ trusting and risk beliefs are associated with their information disclosure. Additionally, the paper reveals that a company’s group and development culture influence these beliefs, with error aversion culture transmitting the effect of development culture. Innovation diffusion relates to the distribution of a novel product, process, or business model across a group of target users. It is important to better understand the diffusion of digital innovations as companies often lack knowledge about why new offerings are rejected, which limits their chances of counteracting the underlying issues. Furthermore, digital technologies impact the innovation diffusion by blurring industry boundaries and facilitating competition. Papers C and D of this thesis investigate the diffusion of digital innovations in the context of data-driven business models. This context is especially affected by new competition arising across previous boundaries and, thus, necessary to analyze as diverse organizations have high incentives to utilize their data in new ways. Paper C analyzes which dimensions substantially differentiate between distinct data-driven business models. For this purpose, it leverages practitioners’ perceptions of business models obtained from a start-up database. Based on three identified dimensions, the paper creates a taxonomy that classifies the business models into eight ideal-typical categories. The number of business models present in each category provides insights into their diffusion. By offering basic knowledge about the nature of data-driven business models, the paper can be used as a foundation for future research that seeks to dig deeper into this new field and for companies that aim at developing data-driven business models. Paper D investigates how individuals evaluate data-driven services that are offered by highly diverse companies. Based on a qualitative study, the paper shows that individuals’ perception of fit between a service and its provider is crucial for their evaluations. It also reveals the dimensions that influence this perception. Additionally, it explores the consequences that come with a perception of fit. Using these results, the paper offers a new perspective on individuals’ service evaluations, which is vital to the diffusion of the services as well as the associated business models and helps organizations in developing and promoting data-driven services

ANALYSING EMPLOYEES’ WILLINGNESS TO DISCLOSE INFORMATION IN ENTERPRISE SOCIAL NETWORKS: THE ROLE OF ORGANISATIONAL CULTURE

Due to the rise of social media, many companies have started to implement enterprise social networks (ESNs). Compared to existing systems supporting communication and collaboration in organisations, ESNs can foster employees’ productivity and innovativeness by making previously invisible communication among employees visible. However, this visibility can prevent employees from disclosing information within ESNs. As the success of ESNs depends on users’ contributions, it is crucial to understand which factors influence employees’ behaviour in this regard. In this research, we investigate the role of organisational culture in fostering employees’ trusting and mitigating their risk beliefs, two factors we transfer from research on Online Social Networks (OSNs) and hypothesize to be highly relevant for information disclosure in ESNs. Based on data obtained from 282 employees, we find sup-port for our hypotheses and illustrate that group and development culture significantly affect employees’ trusting and risk beliefs, and their willingness to disclose information. Our results imply that organisations should carefully assess employees’ trusting and risk beliefs as well as their culture to ac-count for possible obstacles preventing employees’ information disclosure